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Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm

This is the official codebase for the paper "Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022.

Before running the experiments, prepare a python 3 environment with the following packages:

  • argparse
  • imageio
  • matplotlib
  • numpy
  • tensorboard
  • torch

And download the data from https://github.com/jeanfeydy/global-divergences/tree/master/sinkhorn_entropies/data

To run the experiments, execute

python fot_experiment.py --log_dir <path to a directory where plots will be saved> --data_dir <path to a directory where the supplied data (.png files) can be found>

The following parameters can be used for configuration:

  • --random_seed <integer: fixes the seed of random number generation, influences pointcloud sampling from the data>
  • --mu_size <integer: number of points in the red pointcloud>
  • --nu_size <integer: number of points in the blue pointcloud>
  • --tolerance <float: convergence of Sinkhorn is assumed when this tolerance level is reached>
  • --epsilon <float: etropic regularization coefficient>
  • --dataset <string: one of "moons", "densities", "slopes", "crescents">
  • --divergence <string: one of "kl", "reverse_kl", "chi2", "reverse_chi2", "hellinger2", "js", "jeffreys", "triangular">
  • --double or --float: sets single or double precision

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